Many real-world engineering optimization problems not only require the simultaneous optimization of a number of objective functions, but also need to track the changing optimal solutions. These problems are called: Dynamic multi-objective optimization (DMOO) problems. Here, where either the objective functions or the constraints change over time, an optimization algorithm should be able to find, and track the changing set of optimal solutions and approximate the time-varying true Pareto front. Therefore, the DMOO algorithm also has to deal with the problems of a lack of diversity and outdated memory.

The main goal of this session is to emphasize the newest techniques in solving dynamic multi-objective optimization problems and handling current issues. Due to the novelty of DMOO, the session more concentrates on combining DMOO with other hot topics, such as deep learning. Therefore, session aims at providing a forum for researchers in the area of DMOO to exchange new ideas and submit their original and unpublished work. Topics of interest include, but are not limited to:

• Complexity, theoretical analysis, and convergence criterion of DMOO

• Constraint and noise handling methods for DMOO

• Benchmarks and performance measures for DMOO

• Evolutionary dynamic multi-objective optimization

• Dynamic Many-objective optimization

• Dynamic multi-objective Deep Learning techniques

• Fuzzy dynamic multi-objective optimization

• Applications: Bio-medical date modeling, Big data, and …

https://sites.google.com/view/cec2019dmoo/home

Organizers:
Prof. Daming Shi, Shenzhen University
Dr. Marde Helbig, University of Pretoria
Dr. Maysam Orouskhani, Shenzhen University